To improve the performance at a reduced complexity, researchers have
recently turned their attention to machine learning techniques for
solving a variety of problems, such as channel
decoding [1]}, end-to-end
communication [2]}, [3]}, and channel
estimation [4]}. Well-trained neural networks
(NNs) are capable of learning the mapping between the system inputs
and outputs [5]}, [6]}, [7]}, [8]}, [9]}, [10]}. One
of the early attempts along this avenue appeared in [5]}, where
a NN was trained for performing power allocation based on an iterative
weighted MMSE (WMMSE) algorithm. The authors of [6]} proposed a
two-stage training mechanism for maximizing the sum-rate by applying
convolutional neural networks (CNN), and further improved the system
performance through unsupervised learning. In [7]}, the authors
adopted an ensemble of fully connected deep neural networks (DNNs) for
optimizing the transmit power allocation. In [8]}, the
problem of joint antenna selection and hybrid beamforming was first
formulated as a classification problem, and then solved by a deep CNN.
Deep learning was also utilized for jointly optimizing the compressive
channel and hybrid beamforming matrices in [9]}. In
[10]}, the authors designed a novel NN inspired by
GoogleNet, which used parallel complex convolutional blocks for hybrid
beamforming.
